U.S. patent number 9,008,351 [Application Number 13/813,007] was granted by the patent office on 2015-04-14 for method and apparatus for processing image, and medical imaging system employing the apparatus.
This patent grant is currently assigned to Samsung Electronics Co., Ltd.. The grantee listed for this patent is Seok-min Han, Dong-goo Kang, Sung-su Kim, Jae-hyun Kwon, Seong-deok Lee, Hyun-hwa Oh, Young-hun Sung. Invention is credited to Seok-min Han, Dong-goo Kang, Sung-su Kim, Jae-hyun Kwon, Seong-deok Lee, Hyun-hwa Oh, Young-hun Sung.
United States Patent |
9,008,351 |
Kang , et al. |
April 14, 2015 |
Method and apparatus for processing image, and medical imaging
system employing the apparatus
Abstract
A method of processing an image is provided. The method includes
estimating a thickness of an object that includes at least two
materials, from a radiation image taken with radiations of at least
two energy bands; and generating an image by comparing the
estimated thickness to a thickness of a local region and extracting
a region of interest.
Inventors: |
Kang; Dong-goo (Suwon-si,
KR), Han; Seok-min (Seongnam-si, KR), Lee;
Seong-deok (Seongnam-si, KR), Sung; Young-hun
(Hwaseong-si, KR), Kim; Sung-su (Yongin-si,
KR), Oh; Hyun-hwa (Hwaseong-si, KR), Kwon;
Jae-hyun (Hwaseong-si, KR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Kang; Dong-goo
Han; Seok-min
Lee; Seong-deok
Sung; Young-hun
Kim; Sung-su
Oh; Hyun-hwa
Kwon; Jae-hyun |
Suwon-si
Seongnam-si
Seongnam-si
Hwaseong-si
Yongin-si
Hwaseong-si
Hwaseong-si |
N/A
N/A
N/A
N/A
N/A
N/A
N/A |
KR
KR
KR
KR
KR
KR
KR |
|
|
Assignee: |
Samsung Electronics Co., Ltd.
(Suwon-si, KR)
|
Family
ID: |
45530637 |
Appl.
No.: |
13/813,007 |
Filed: |
July 29, 2011 |
PCT
Filed: |
July 29, 2011 |
PCT No.: |
PCT/KR2011/005631 |
371(c)(1),(2),(4) Date: |
January 29, 2013 |
PCT
Pub. No.: |
WO2012/015280 |
PCT
Pub. Date: |
February 02, 2012 |
Prior Publication Data
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|
|
|
Document
Identifier |
Publication Date |
|
US 20130129180 A1 |
May 23, 2013 |
|
Foreign Application Priority Data
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|
|
|
Jul 29, 2010 [KR] |
|
|
10-2010-0073697 |
|
Current U.S.
Class: |
382/100; 382/274;
600/476 |
Current CPC
Class: |
A61B
6/502 (20130101); G16H 50/30 (20180101); A61B
6/482 (20130101); G06T 7/0012 (20130101); A61B
6/5217 (20130101); G06T 2207/10116 (20130101); G06T
2207/30068 (20130101) |
Current International
Class: |
G06K
9/00 (20060101); A61B 6/00 (20060101) |
Field of
Search: |
;382/100,103,106-107,128,134,162,168,172-173,181,194,232,254,258,274,276,291,305,312
;600/476 ;378/20,21,5,53 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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1738573 |
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Feb 2006 |
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CN |
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101023875 |
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Aug 2007 |
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CN |
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63-247870 |
|
Oct 1988 |
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JP |
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5-99829 |
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Apr 1993 |
|
JP |
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05-161631 |
|
Jun 1993 |
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JP |
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2002-530171 |
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Sep 2002 |
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JP |
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2003-515376 |
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May 2003 |
|
JP |
|
2006-519625 |
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Aug 2006 |
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JP |
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2007-111525 |
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May 2007 |
|
JP |
|
2008-161690 |
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Jul 2008 |
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JP |
|
Other References
International Search Report issued Apr. 26, 2012 in counterpart
International Patent Application No. PCT/KR2011/005631 (3 pages, in
English). cited by applicant .
Japanese Notice of Allowance issued on Mar. 28, 2014 in
corresponding Japanese Application No. 2013-521719 (6 pages with
English translation). cited by applicant .
Chinese Office Action issued Jul. 17, 2014 in corresponding Chinese
Application No. 201180037280.8 (12 pages with English translation).
cited by applicant.
|
Primary Examiner: Azarian; Seyed
Attorney, Agent or Firm: NSIP Law
Claims
What is claimed is:
1. A method of processing an image, the method comprising:
estimating a thickness of an object that includes at least two
materials, from a radiation image taken with radiations of at least
two energy bands; and generating an image by comparing the
estimated thickness to a thickness of a local region and extracting
a region of interest; wherein the estimating of the thickness is
performed by applying a total thickness model obtained by using a
continuous thickness phantom image having a combination of two
materials.
2. The method of claim 1, wherein the region of interest is an
abnormal tissue.
3. The method of claim 2, wherein the two materials are materials
of normal tissues.
4. The method of claim 1, wherein the local region is adjacent to a
region where the thickness is estimated.
5. The method of claim 1, wherein the total thickness model has a
density of any one of the two materials as a parameter.
6. The method of claim 1, wherein the estimating of the thickness
comprises: estimating densities of the two materials; estimating
thicknesses of each of the two materials from the radiation image
by using the estimated densities and attenuation bases of the two
materials; and estimating the thickness of the object by summing
the thicknesses of the two materials.
7. A non-transitory computer-readable medium, the medium storing a
program that causes a computer including a processor to perform the
method of claim 1.
8. An apparatus for processing an image, the apparatus comprising:
a thickness estimating unit configured to estimate a thickness of
an object including at least two materials, from a radiation image
taken with radiations of at least two energy bands; and an image
generating unit configured to generate an image by comparing the
estimated thickness to a thickness of a local region and extracting
a region of interest; wherein the thickness estimating unit
estimates the thickness by applying a total thickness model
obtained by using a continuous thickness phantom image having a
combination of two materials.
9. The apparatus of claim 8, wherein the region of interest is an
abnormal tissue.
10. The apparatus of claim 9, wherein the two materials are
materials of normal tissues.
11. The apparatus of claim 8, wherein the local region is adjacent
to a region where the thickness is estimated.
12. The apparatus of claim 8, wherein the total thickness model has
a density of any one of the two materials as a parameter.
13. The apparatus of claim 8, wherein the thickness estimating unit
estimates densities of the two materials, estimates thickness of
each of the two materials from the radiation image by using the
estimated densities and attenuation bases of the two materials, and
estimates the thickness of the object by summing the thicknesses of
the two materials.
14. A medical imaging apparatus for processing an image, the
apparatus comprising: a thickness estimating unit configured to
estimate a thickness of an object including at least two materials,
from a radiation image taken with radiations of at least two energy
bands; and an image generating unit configured to generate an image
by comparing the estimated thickness to a thickness of a local
region and extracting a region of interest; wherein the thickness
estimating unit estimates the thickness by applying a total
thickness model obtained by using a continuous thickness phantom
image having a combination of two materials.
15. The medical imaging apparatus of claim 14, wherein the local
region is adjacent to a region where the thickness is
estimated.
16. The medical imaging apparatus of claim 14, further comprising a
radiation image obtaining unit configured to obtain the radiation
image by irradiating radiations of at least two energy bands onto
the object from the same angle.
17. The medical imaging apparatus of claim 14, further comprising a
storage unit configured to store the generated image, or configured
to store diagnosis information obtained from the generated
image.
18. The medical imaging apparatus of claim 14, further comprising a
communication unit configured to transmit the generated image, or
configured to transmit diagnosis information obtained from the
generated image.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
This application is a national stage of International Application
No. PCT/KR2011/005631 filed Jul. 29, 2011, claiming priority based
on Korean Patent Application No. 10-2010-0073697 filed Jul. 29,
2010, the contents of all of which are incorporated herein by
reference in their entirety.
BACKGROUND
1. Field
The following description relates to a method and an apparatus for
processing an image, and, for example, to a medical imaging system
employing such an apparatus.
2. Description of Related Art
A radiation image, such as an X-ray image of a predetermined
subject like the body of a patient, is obtained by the varying
transmittance of X-ray through different types of materials and
densities of the materials present in the body of the patient, as
well as the energy band of the X-ray. As a result, sometimes,
certain tissues or materials in a patient's body are not easily
identified by using only one X-ray image. In particular, normal and
abnormal tissues in a patient's body may not be easily identified
from an X-ray image.
SUMMARY
In one general aspect, there is provided a method of processing an
image involving: estimating a thickness of an object that includes
at least two materials, from a radiation image taken with
radiations of at least two energy bands; and generating an image by
comparing the estimated thickness to a thickness of a local region
and extracting a region of interest.
The region of interest may be an abnormal tissue.
The two materials may be materials of normal tissues.
The local region may be adjacent to a region where the thickness is
estimated.
The estimating of the thickness may be performed by applying a
total thickness model obtained by using a continuous thickness
phantom image having a combination of two materials.
The total thickness model may have a density of any one of the two
materials as a parameter.
The estimating of the thickness may comprise: estimating densities
of the two materials; estimating thicknesses of each of the two
materials from the radiation image by using the estimated densities
and attenuation bases of the two materials; and estimating the
thickness of the object by summing the thicknesses of the two
materials.
In another general aspect, there is provided an apparatus for
processing an image, the apparatus including: a thickness
estimating unit to estimate a thickness of an object including at
least two materials, from a radiation image taken with radiations
of at least two energy bands; and an image generating unit to
generate an image by comparing the estimated thickness to a
thickness of a local region and extracting a region of
interest.
The region of interest may be an abnormal tissue.
The two materials may be materials of normal tissues.
The local region may be adjacent to a region where the thickness is
estimated.
The thickness estimating unit may estimate the thickness by
applying a total thickness model obtained by using a continuous
thickness phantom image having a combination of two materials.
The total thickness model may have a density of any one of the two
materials as a parameter.
The thickness estimating unit may estimate densities of the two
materials, may estimate thickness of each of the two materials from
the radiation image by using the estimated densities and
attenuation bases of the two materials, and may estimate the
thickness of the object by summing the thicknesses of the two
materials.
In another general aspect, there is provided a medical imaging
system having an apparatus for processing an image, the apparatus
including: a thickness estimating unit to estimate a thickness of
an object including at least two materials, from a radiation image
taken with radiations of at least two energy bands; and an image
generating unit to generate an image by comparing the estimated
thickness to a thickness of a local region and extracting a region
of interest.
The local region may be adjacent to a region where the thickness is
estimated.
The medical imaging system may further include a radiation image
obtaining unit to obtain the radiation image by irradiating
radiations of at least two energy bands onto the object from the
same angle.
The medical imaging system may further include a storage unit to
store the generated image, or to store diagnosis information
obtained from the generated image.
The medical imaging system may further include a communication unit
to transmit the generated image, or transmit diagnosis information
obtained from the generated image.
In another general aspect, there is provided a non-transitory
computer-readable medium, the medium storing a program that causes
a computer including a processor to perform the method described
above.
Other features and aspects may be apparent from the following
detailed description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a graph illustrating X-ray attenuation coefficients of
normal and abnormal body tissues in energy bands.
FIG. 2 is a graph illustrating an attenuation basis model of a body
constituent material.
FIG. 3 is a diagram illustrating an example of a method of
separating three materials based on information regarding a
thickness of an object.
FIG. 4 is a flowchart illustrating an example of a method of
processing an image.
FIG. 5 is a diagram illustrating an example of a method of
maximizing uniformity of a thickness of a breast by using a total
variation.
FIG. 6 is a conceptual diagram illustrating an example of a method
of setting a total thickness model having one parameter by using a
continuous thickness phantom image having a combination of two
materials.
FIG. 7 is a block diagram illustrating an example of a medical
imaging system according to a general aspect.
FIG. 8 is a block diagram illustrating an example of an apparatus
for processing an image.
Throughout the drawings and the detailed description, unless
otherwise described, the same drawing reference numerals will be
understood to refer to the same elements, features, and structures.
The relative size and depiction of these elements may be
exaggerated for clarity, illustration, and convenience.
DETAILED DESCRIPTION
The following detailed description is provided to assist the reader
in gaining a comprehensive understanding of the methods,
apparatuses, and/or systems described herein. Accordingly, various
changes, modifications, and equivalents of the systems, apparatuses
and/or methods described herein will be suggested to those of
ordinary skill in the art. Also, descriptions of well-known
functions and constructions may be omitted for increased clarity
and conciseness.
Described below are examples of methods and apparatuses for
processing an image to obtain a diagnostic image. The diagnostic
image may depict the image of an abnormal tissue that is extracted
from a multi-energy radiation image that depicts a plurality of
mixed materials in the body of a patient.
Also described below are examples of medical imaging systems
employing such apparatus.
According to one or more of the examples described hereafter, since
a region of interest, such as an abnormal tissue, is extracted from
a multi-energy radiation image that depicts a plurality of mixed
materials in a portion of a patient's body, a high-resolution
diagnostic image may be generated by emphasizing the extracted
abnormal tissue. Thus, the accuracy of diagnosis may be increased
even when only one radiation image is used for diagnosis.
Correlations between an incident intensity and a transmitted
intensity of a radiation image, such as an X-ray image, are now
described.
The rate of transmittance of an X-ray through an object differs
according to the types of and densities of materials found in the
object, as well as an energy band of the X-ray. The incident
intensity and the transmitted intensity may be represented as shown
in Equation 1. <Equation 1>
I(E)=I.sub.0(E)e.sup.-.mu.(E).rho.x
Here, I(E) represents a transmitted intensity, and I.sub.0(E)
represents an incident intensity. .mu.(E) represents a mass
attenuation coefficient in an energy band E, .rho. represents the
density of an object, and x represents the thickness of the object.
Equation 1 is referred to as the Beer-Lambert law.
FIG. 1 illustrates X-ray attenuation coefficients of normal and
abnormal body tissues in different energy bands. Referring to FIG.
1, a fat tissue, a glandular tissue, and an infiltrating ductal
carcinoma (IDC) tissue have different X-ray attenuation
coefficients at different energy bands.
In the case where a radiation, such as an X-ray, is used to scan an
object at least two energy bands, energy band images I.sub.1
through I.sub.n (n is the number of energy bands) may be obtained.
The energy band images I.sub.1 through I.sub.n form one radiation
image mathematically represented as shown in Equation 2.
.intg..times..function..times..times..times..mu..function..times.d.intg..-
times..function..times..times..times..mu..function..times.d.intg..times..f-
unction..times..times..times..mu..function..times.d.times..times.
##EQU00001##
Here, w.sub.n is an incident intensity of an X-ray in the n-th
energy band, and C.sub.i satisfies C.sub.i=.intg..sub.Lc.sub.i(r)dr
and c.sub.i(r) represents a material density projection at each
position vector r.
In Equation 2, if the energy band images I.sub.1 through I.sub.n
are obtained, normal and abnormal body materials in a patient's
body may be separated by calculating the material density
projection from each of the energy band images I.sub.1 through
I.sub.n.
Meanwhile, since the number of attenuation bases of body
constituent materials is two (i.e., photoelectric absorption and
Compton scattering), if the maximum number of mixed materials is
two, the materials may be separated.
FIG. 2 is a graph illustrating an attenuation basis model of a body
constituent material, for example, water. Referring to FIG. 2, a
physical model may be represented as shown in Equation 3.
<Equation 3> .mu.(E,{right arrow over (r)})=c.sub.1({right
arrow over (r)})f.sub.ph(E)+c.sub.2({right arrow over
(r)})f.sub.Co(E)
Meanwhile, an experimental model may be represented as shown in
Equation 4. <Equation 4> .mu.(E,{right arrow over
(r)})=c.sub.1({right arrow over
(r)}).mu..sub.water(E)+c.sub.2({right arrow over
(r)}).mu..sub.bon
In Equations 3 and 4, .mu.(E,{right arrow over (r)}) represents an
attenuation coefficient of a position vector r in an energy band E,
c.sub.1({right arrow over (r)}) and c.sub.2({right arrow over (r)})
represent lengths (thicknesses) of the position vector r of
tissues, f.sub.ph(E) and f.sub.Co(E) and represent basis functions,
i.e., attenuation coefficients, according to photoelectric
absorption and Compton scattering in the energy band E, and
.mu..sub.water(E) and .mu..sub.bon respectively represent
attenuation coefficients of water and bone in the energy band
E.
Meanwhile, normal tissues of, for example, a breast from among
organs of a body may mainly include an adipose tissue and a
glandular tissue. If an abnormal tissue such as a mass tissue is
mixed, a total of three materials may be included in a multi-energy
radiation image. In this example, three materials may be separated
from each other by using the overall thickness information of the
object, which is a breast in this case, and the attenuation bases
of body constituent materials determined through, i.e.,
photoelectric absorption and Compton scattering.
For example, if attenuation bases of body constituent materials,
i.e., photoelectric absorption and Compton scattering, are used, an
attenuation coefficient .mu.(E,{right arrow over (r)}) of a
position vector r in an energy band E may be represented as shown
in Equation 5.
.mu..function..fwdarw..times..function..fwdarw..times..mu..function..fwda-
rw..times..mu..function..fwdarw..times..mu..times..alpha..times..times..ti-
mes..times..function..beta..times..times..function..times..times.
##EQU00002##
Thus, three different materials, including, for example, an adipose
tissue A, a glandular tissue G, and an abnormal tissue C such as a
cancerous tissue, may not be separated from one another. In this
example, c.sub.1({right arrow over (r)}), c.sub.2({right arrow over
(r)}), and c.sub.3({right arrow over (r)}) are proportional
constants representing thicknesses of tissues, .mu..sub.A,
.mu..sub.G, and .mu..sub.C represent attenuation coefficients of
the adipose tissue A, the glandular tissue G, and the abnormal
tissue C. .alpha. and .beta. represent proportional constants,
f.sub.ph(E) represents a basis function, i.e., an attenuation
coefficient, according to photoelectric absorption in an energy
band E, and f.sub.Co(E) represents a basis function, i.e., an
attenuation coefficient, according to Compton scattering in the
energy band E.
Meanwhile, a total thickness T of an object may be represented as
shown in Equation 6. <Equation 6> c.sub.1({right arrow over
(r)})+c.sub.2({right arrow over (r)})+c.sub.3({right arrow over
(r)})=T
If the total thickness T of the object is used, an attenuation
coefficient .mu.(E,{right arrow over (r)}) of a position vector r
in an energy band E may be represented as shown in Equation 7.
<Equation 7> .mu.(E,{right arrow over (r)})=c.sub.1({right
arrow over (r)})(.mu..sub.A-.mu..sub.C)+c.sub.2({right arrow over
(r)})(.mu..sub.G-.mu..sub.C)+T.mu..sub.C
Thus, three materials may be separated.
FIG. 3 illustrates an example of a method of separating three
different materials present in an object by using information
regarding a total thickness of the object. The method may rely on
the fact that a total thickness of a body part such as, for
example, a compressed breast, is almost constant. For example, a
glandular tissue G and an adipose tissue A captured in a radiation
image may be separated from one another by using attenuation bases
of normal materials such as the glandular tissue G and the adipose
tissue A. If the separated glandular tissue G and the adipose
tissue A are combined, a total thickness image may be obtained.
That is, in a normal region, the total thickness image has the same
thickness as a total thickness of the depicted object, i.e., a
breast of a patient; however, in an abnormal region, the calculated
total thickness image is different from the calculated thickness of
the normal region.
Referring to FIG. 3, if a region 310 including an adipose tissue A
and a glandular tissue G, and a region 320 including the adipose
tissue A, the glandular tissue G, and an abnormal tissue C such as
a cancerous tissue respectively have a grayscale intensity 330 and
a grayscale intensity 340, the adipose tissue A and the glandular
tissue G may be separated from each of the grayscale intensity 330
and the grayscale intensity 340 by using attenuation bases of the
adipose tissue A and the glandular tissue G. A thickness 350
represents the adipose tissue A and the glandular tissue G
separated from the grayscale intensity 330, and a thickness 360
represents the adipose tissue A and the glandular tissue G
separated from the grayscale intensity 340. As described above, if
the separated glandular tissue G and the adipose tissue A are
combined, the region 310 may have a thickness 370 that is the same
as a total thickness of a breast. Thus, the region 310 may be
determined as a normal region. However, the region 320 has a
calculated thickness 380 that is different from the total thickness
of the breast; thus, the region 320 may be determined as an
abnormal region.
FIG. 4 is a flowchart illustrating an example of a method of
processing an image according to a general aspect.
Referring to FIG. 4, in operation 410, a multi-energy radiation
image taken with a radiation having at least two energy bands of a
predetermined subject, such as a body part of a patient, may be
received.
In operation 430, the densities of at least two materials included
in the multi-energy radiation image may be estimated.
In operation 450, the two materials such as a glandular tissue and
an adipose tissue may be separated from the multi-energy radiation
image by using the estimated densities of the materials and
attenuation bases of the materials.
In operation 470, a total thickness of a region of the body part
may be estimated by combining the estimated thickness of the
separated glandular tissue and the adipose tissue at the region.
Further, although not shown in FIG. 4, in some examples, a
uniformity level of the thickness of the body part may be measured
by using information regarding the combined thickness estimated in
operation 470. In addition, parameters such as the densities of the
materials may be updated based on the measured uniformity level and
then may be fed back to operation 430.
In operation 490, an abnormal region having an abnormal tissue,
such as a mass or microcalcification tissue, may be extracted by
comparing the combined thickness to a thickness calculated at a
local region of the multi-energy radiation image. A diagnostic
image may be generated based on using the information about the
extracted abnormal region.
Meanwhile, a total thickness image may be obtained by substituting
a total thickness model for operations 430, 450, and 470,
representing the total thickness model as a polynomial having a
certain number of parameters, and optimizing each parameter to
maximize the uniformity of a total thickness. In this example, a
total thickness model represented as a linear polynomial may be
represented as shown in Polynomial 8. <Polynomial 8>
a.sub.1I.sub.L+a.sub.2I.sub.H+a.sub.3
A total thickness model represented as a quadratic polynomial may
be represented as shown in Polynomial 9. <Polynomial 9>
a.sub.1I.sub.L+a.sub.2I.sub.H+a.sub.3+a.sub.4I.sub.L.sup.2+a.sub.6I.sub.L-
I.sub.H
In Polynomials 8 and 9, I.sub.L and I.sub.H represent a radiation
image of a low energy band and a radiation image of a high energy
band, respectively and a.sub.1, a.sub.2, a.sub.3, a.sub.4, a.sub.5,
and a.sub.6 represent optimized proportional constants.
FIG. 5 illustrates an example of a method of maximizing uniformity
of a total thickness of a breast by using a total variation.
Referring to FIG. 5, if a linear polynomial image 530 may be
represented by minimizing a total variation L1-norm of a radiation
image 510 of the breast, the uniformity of the total thickness of
the breast may be maximized.
Meanwhile, a total thickness of a body part, i.e., the breast, may
be approximated by using a polynomial, for example, Polynomial 8 or
Polynomial 9. In this example, if the order of the polynomial is
high, the accuracy of approximation may be increased while the
capability of convergence may be reduced because a plurality of
parameters should be optimized. Accordingly, a total thickness
model having a high accuracy of approximation and a small number of
parameters needs to be set. For this, a total thickness model
having one parameter may be set by using a continuous thickness
phantom image having a combination of two materials.
FIG. 6 is a conceptual diagram illustrating a method of setting a
total thickness model having one parameter by using a continuous
thickness phantom image having a combination of two materials.
Referring to FIG. 6, main constituent materials of a body part, for
example, a breast, may be an adipose tissue and a glandular tissue,
and the density of each tissue may differ according to an
individual person. In order to calculate a total thickness model
capable of reflecting the difference in density, initially, a
continuous thickness phantom image 600 having an average reference
density of any one material, for example, the glandular tissue may
be captured, and a phantom image I(n) of each energy band and a
thickness image T(m) corresponding to the phantom image I(n) may be
obtained. Then, it is assumed that the phantom image I(n) is
obtained when a density d of the glandular tissue has a certain
value, i.e., an average reference density r, and a thickness image
T'(m) corresponding to the phantom image I(n) is obtained. In this
example, T'(m)=T(m)/r. If an approximation polynomial coefficient
between the phantom image I(n) and the thickness image T'(m) is P',
the approximation polynomial coefficient P' will be almost
identical to a polynomial coefficient by using a phantom image
actually captured from a portion where the density d of the
glandular tissue is r and a thickness image corresponding to the
phantom image. Accordingly, a polynomial coefficient at an
arbitrary density of the glandular tissue may be calculated by
using one phantom image having an average reference density without
capturing a phantom image at every density of the glandular
tissue.
Consequently, a total thickness of an object may be represented by
using one parameter such as the density d, and may be applied to a
polynomial model shown in Polynomials 8 and 9. That is, if the
total thickness of the object is f(x), approximation may be
performed as f(x)=Polynomial 8, or f(x)=Polynomial 9.
FIG. 7 illustrates an example of a medical imaging system according
to a general aspect. The medical imaging system may include a
radiation image obtaining unit 710, an image processing unit 730, a
display unit 750, a storage unit 770, and a communication unit 90.
In this example, the medical imaging system may be implemented by
using only the image processing unit 730. That is, the radiation
image obtaining unit 710, the display unit 750, the storage unit
770, and the communication unit 790 may be optionally included.
Meanwhile, the image processing unit 730 may be implemented as at
least one processor.
Referring to FIG. 7, the radiation image obtaining unit 710 may be
configured to irradiate a radiation having at least two different
energy bands onto an object and to capture a multi-energy radiation
image of the object. If a radiation, for example, an X-ray, having
different energy bands is irradiated onto the same tissue of a
patient, the radiation may be absorbed or scattered by the tissue
to different degrees. By using this property, a multi-energy
radiation image to which different absorption properties according
to energy bands are reflected may be obtained by irradiating an
X-ray having two or more energy bands onto each tissue.
Meanwhile, if the radiation image obtaining unit 710 may not be
included in the medical imaging system, a multi-energy radiation
image provided from outside the medical imaging system may be input
to the image processing unit 730.
The image processing unit 730 may extract an abnormal tissue by
comparing a total thickness of an object in the multi-energy
radiation image provided from the radiation image obtaining unit
710 or outside the medical imaging system, to a thickness
calculated at a local region of the multi-energy radiation image,
and generate a diagnostic image including the extracted abnormal
tissue. That is, normal tissues may be removed from the diagnostic
image generated by the image processing unit 730. Meanwhile, the
image processing unit 730 may perform noise reduction on the
multi-energy radiation image, or may perform noise reduction and/or
contrast enhancement on the diagnostic image. Meanwhile, the image
processing unit 730 may also have an image reading function, and
thus may obtain required diagnosis information from the diagnostic
image.
The display unit 750 may be implemented as, for example, a monitor,
and may display the diagnostic image generated by the image
processing unit 730, or may display the diagnosis information
obtained by the image processing unit 730 together with the
diagnostic image.
The storage unit 770 may be implemented as, for example, a
non-transitory memory, and may store the diagnostic image generated
by the image processing unit 730, or may store the diagnosis
information obtained by the image processing unit 730, in
correspondence with the diagnostic image.
The communication unit 790 may transmit by a wired or wireless
network the diagnostic image generated by the image processing unit
730, or the diagnostic image combined with the diagnosis
information to another medical imaging system located at a remote
place or a specialist such as a doctor at a hospital, and may
receive and input the multi-energy radiation image provided from
outside the medical imaging system, to the image processing unit
730. In particular, the communication unit 790 may transmit by a
wired or wireless network the diagnostic image, or the diagnostic
image combined with the diagnosis information to another medical
imaging system or a specialist who has transmitted the multi-energy
radiation image.
Meanwhile, the storage unit 770 and the communication unit 790 may
be integrated into a picture archiving communication system (PACS)
by adding image reading and searching functions.
Alternatively, the image processing unit 730, the storage unit 770,
and the communication unit 790 may be integrated into a PACS.
Meanwhile, the medical imaging system may be any image diagnostic
system using a radiation, for example, an X-ray. For example, the
medical imaging system 700 may be a mammographic image diagnostic
system used to determine a lesion of breasts including only soft
tissues without any bone in a body.
FIG. 8 illustrates an example of an apparatus for processing an
image according to a general aspect. The image processing apparatus
may include a thickness estimating unit 810 and an image generating
unit 830. In this example, the thickness estimating unit 810 and
the image generating unit 830 may be implemented as at least one
processor.
Referring to FIG. 8, the thickness estimating unit 810 may estimate
a total thickness of an object from an input multi-energy radiation
image. In this example, the total thickness may be estimated by
using a continuous thickness phantom image having a combination of
two materials, a total thickness model having a density of any one
of the two materials as a parameter may be applied to the
multi-energy radiation image.
The image generating unit 830 may compare the total thickness
estimated by the thickness estimating unit 810 to a thickness
calculated at a local region of the multi-energy radiation image,
may extract a region of interest, e.g., an abnormal tissue,
according to a comparison result, and may generate a diagnostic
image including the extracted abnormal tissue.
As described above, according to one or more of the above examples,
since a region of interest, e.g., an abnormal tissue, is extracted
from a multi-energy radiation image in which a plurality of
materials are mixed, and a high-resolution diagnostic image may be
generated by emphasizing the extracted abnormal tissue, the
accuracy of diagnosis may be increased even when only one radiation
image is used.
As described above, according to one or more of the above examples,
a method of processing an image is provided. The method includes
estimating a total thickness of an object including at least two
materials, from a radiation image, that is images taken from one
position, obtained with radiations of at least two energy bands and
obtained from the same object; and generating a diagnostic image by
comparing the estimated total thickness to a thickness of a local
region of the radiation image, and extracting a region of interest
according to a comparison result. The region of interest may be an
abnormal tissue. The two materials may be materials of normal
tissues.
The estimating of the total thickness may be performed by applying
a total thickness model obtained by using a continuous thickness
phantom image having a combination of two materials. The total
thickness model may have a density of any one of the two materials
as a parameter.
The estimating of the total thickness may include estimating
densities of the two materials; separating the two materials from
the multi-energy radiation image by using the estimated densities
and attenuation bases of the two materials; and estimating the
total thickness of the object by summing thicknesses of the two
separated materials.
According to another example, an apparatus for processing an image
is provided. The apparatus includes a thickness estimating unit to
estimate a total thickness of an object including at least two
materials, from a radiation image taken with radiations having at
least two energy bands; and an image generating unit to generate a
diagnostic image by comparing the estimated total thickness to a
thickness of a local region, and extracting a region of interest
according to a comparison result.
According to still another example, a medical imaging system is
provided. The system includes an apparatus for processing an image,
the apparatus including a thickness estimating unit to estimate a
total thickness of an object that includes at least two materials,
from a radiation image taken with radiations of at least two energy
bands and obtained from the same object; and an image generating
unit to generate a diagnostic image by comparing the estimated
total thickness to a thickness of a local region of the radiation
image, and extracting a region of interest according to a
comparison result.
The medical imaging system may further include a radiation image
obtaining unit to obtain the radiation image by irradiating a
radiation having at least two energy bands onto the object.
The medical imaging system may further include a storage unit to
store the generated diagnostic image, or to store diagnosis
information obtained from the generated diagnostic image, in
correspondence with the diagnostic image.
The medical imaging system may further include a communication unit
to transmit the generated diagnostic image, or transmit diagnosis
information obtained from the generated diagnostic image, in
correspondence with the diagnostic image.
Program instructions to perform a method described herein, or one
or more operations thereof, may be recorded, stored, or fixed in
one or more computer-readable storage media. The program
instructions may be implemented by a computer. For example, the
computer may cause a processor to execute the program instructions.
The media may include, alone or in combination with the program
instructions, data files, data structures, and the like. Examples
of non-transitory computer-readable media include magnetic media,
such as hard disks, floppy disks, and magnetic tape; optical media
such as CD ROM disks and DVDs; magneto-optical media, such as
optical disks; and hardware devices that are specially configured
to store and perform program instructions, such as read-only memory
(ROM), random access memory (RAM), flash memory, and the like.
Examples of program instructions include machine code, such as
produced by a compiler, and files containing higher level code that
may be executed by the computer using an interpreter. The program
instructions, that is, software, may be distributed over network
coupled computer systems so that the software is stored and
executed in a distributed fashion. For example, the software and
data may be stored by one or more computer readable recording
mediums. Also, functional programs, codes, and code segments for
accomplishing the examples disclosed herein can be easily construed
by programmers skilled in the art to which the examples pertain
based on and using the flow diagrams and block diagrams of the
figures and their corresponding descriptions as provided herein.
Also, the described unit to perform an operation or a method may be
hardware, software, or some combination of hardware and software.
For example, the unit may be a software package running on a
computer or the computer on which that software is running.
A number of examples have been described above. Nevertheless, it
will be understood that various modifications may be made. For
example, suitable results may be achieved if the described
techniques are performed in a different order and/or if components
in a described system, architecture, device, or circuit are
combined in a different manner and/or replaced or supplemented by
other components or their equivalents. Accordingly, other
implementations are within the scope of the following claims.
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